scikit-gstat 0.2.2

Geostatistical expansion in the scipy style

SciKit-GStat
============

Info: scikit-gstat needs Python >= 3.4!

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How to cite
-----------

In case you use SciKit-GStat in other software or scientific publications,
please reference this module. It is published and has a DOI. It can be cited
as:

Mälicke, Mirko, & Schneider, Helge David. (2018). mmaelicke/scikit-gstat:
Geostatistical variogram toolbox (Version v0.2.2). Zenodo.
http://doi.org/10.5281/zenodo.1345584

Full Documentation
------------------

The full documentation can be found at: https://mmaelicke.github.io/scikit-gstat


New Version 0.2
---------------

Scikit-gstat was rewritten in major parts. Most of the changes are internal,
but the attributes and behaviour of the `Variogram` has also changed
substantially.
A detailed description of of the new versions usage will follow. The last
version of the old Variogram class, 0.1.8, is kept in the `version-0.1.8`
branch on GitHub, but not developed any further. Those two versions are not
compatible.

Description
-----------

SciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes
two base classes :class:`Variogram <skgstat.Variogram>` and
:class:`DirectionalVariogram <skgstat.DirectionalVariogram>`. Both have a
very similar interface and can compute experimental variograms and model
variograms. The module makes use of a rich selection of semi-variance
estimators and variogram model functions, while being extensible at the same
time.
The estimators include:

- matheron
- cressie
- dowd
- genton
- entropy
- two experimental ones: quantiles, minmax

The models include:

- sperical
- exponential
- gaussian
- cubic
- stable
- matérn

with all of them in a nugget and no-nugget variation. All the estimator are
implemented using numba's jit decorator. The usage of numba might be subject
to change in future versions.
At the current stage, the package does not include any kriging. This is planned for a future release.


Installation
~~~~~~~~~~~~

PyPI:

.. code-block:: bash

pip install scikit-gstat

GIT:

.. code-block:: bash

git clone https://github.com/mmaelicke/scikit-gstat.git
cd scikit-gstat
pip install -r requirements.txt
pip install -e .

Usage
~~~~~

The `Variogram` class needs at least a list of coordiantes and values. All other attributes are set by default.
You can easily set up an example by generating some random data:

.. code-block:: python

import numpy as np
import skgstat as skg

coordinates = np.random.gamma(0.7, 2, (30,2))
values = np.random.gamma(2, 2, 30)

V = skg.Variogram(coordinates=coordinates, values=values)
print(V)

.. code-block:: bash

spherical Variogram
-------------------
Estimator: matheron
Range: 1.64
Sill: 5.35
Nugget: 0.00